Patch Matching
40 papers with code • 2 benchmarks • 4 datasets
Most implemented papers
Working hard to know your neighbor's margins: Local descriptor learning loss
We introduce a novel loss for learning local feature descriptors which is inspired by the Lowe's matching criterion for SIFT.
Rotation equivariant vector field networks
In many computer vision tasks, we expect a particular behavior of the output with respect to rotations of the input image.
Distinctive Image Features from Scale-Invariant Keypoints
This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene.
MatchNet: Unifying Feature and Metric Learning for Patch-Based Matching
We perform a comprehensive set of experiments on standard datasets to carefully study the contributions of each aspect of MatchNet, with direct comparisons to established methods.
Continuous 3D Label Stereo Matching using Local Expansion Moves
The local expansion moves extend traditional expansion moves by two ways: localization and spatial propagation.
On Translation Invariance in CNNs: Convolutional Layers can Exploit Absolute Spatial Location
In this paper we challenge the common assumption that convolutional layers in modern CNNs are translation invariant.
Attention Concatenation Volume for Accurate and Efficient Stereo Matching
Stereo matching is a fundamental building block for many vision and robotics applications.
IGEV++: Iterative Multi-range Geometry Encoding Volumes for Stereo Matching
To construct MGEV, we introduce an adaptive patch matching module that efficiently and effectively computes matching costs for large disparity ranges and/or ill-posed regions.
Person Re-identification with Correspondence Structure Learning
This paper addresses the problem of handling spatial misalignments due to camera-view changes or human-pose variations in person re-identification.
Deep Colorization
This paper investigates into the colorization problem which converts a grayscale image to a colorful version.